etecting cognitive impairment by eye movement analysis using automatic lassification algorithms
etecting cognitive impairment by eye movement analysis using automatic lassification algorithms
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发表时间：
2011
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通讯作者：
Dmitry Lagun;Cecelia M. Manzanares;S. Zola;E. Buffalo;Eugene Agichtein
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作者：
Dmitry Lagun;Cecelia M. Manzanares;S. Zola;E. Buffalo;Eugene Agichtein
视觉配对比较（VPC）任务是一种识别记忆测试，已显示出用于检测与轻度认知障碍（MCI）相关的记忆障碍的前景。由于MCI患者经常进展为阿尔茨海默病（AD），VPC可能有助于预测AD的发作。VPC使用非侵入性眼动跟踪来识别受试者如何看待新的和重复的视觉刺激。健康的对照受试者通过花费更多的时间观看新图像来展示对重复刺激的记忆，即，新奇偏好在这里，我们报告了计算机科学中的机器学习方法的应用，通过对VPC任务期间的注视、扫视和重新注视等眼动特征进行建模来提高检测MCI的准确性。这些特征被表示为提供给诸如支持向量机（SVM）的自动分类算法的特征。使用SVM分类算法，结合对注视、扫视方向和回归模式的模式进行建模，我们的算法能够以87%的准确性、97%的灵敏度和77%的特异性自动区分年龄匹配的正常对照受试者和MCI受试者，相比之下，最好的分类性能为67%的准确性、60%的灵敏度，当仅使用新奇偏好信息时，特异性为73%。这些结果证明了应用机器学习技术检测MCI的有效性，并提出了一种有前途的方法，用于检测与其他疾病相关的认知障碍。
The Visual Paired Comparison (VPC) task is a recognition memory test that has shown promise for the detection of memory impairments associated with mild cognitive impairment (MCI). Because patients with MCI often progress to Alzheimer’s Disease (AD), the VPC may be useful in predicting the onset of AD. VPC uses noninvasive eye tracking to identify how subjects view novel and repeated visual stimuli. Healthy control subjects demonstrate memory for the repeated stimuli by spending more time looking at the novel images, i.e., novelty preference. Here, we report an application of machine learning methods from computer science to improve the accuracy of detecting MCI by modeling eye movement characteristics such as fixations, saccades, and re-fixations during the VPC task. These characteristics are represented as features provided to automatic classification algorithms such as Support Vector Machines (SVMs). Using the SVM classification algorithm, in tandem with modeling the patterns of fixations, saccade orientation, and regression patterns, our algorithm was able to automatically distinguish age-matched normal control subjects from MCI subjects with 87% accuracy, 97% sensitivity and 77% specificity, compared to the best available classification performance of 67% accuracy, 60% sensitivity, and 73% specificity when using only the novelty preference information. These results demonstrate the effectiveness of applying machinelearning techniques to the detection of MCI, and suggest a promising approach for detection of cognitive impairments associated with other disorders.